2 March 2017
TOC
Overview
What does the Kalman filter do?
Kalman filtering, also known as linear quadratic estimation (LQE), is an algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more accurate than those based on a single measurement alone, by using Bayesian inference and estimating a joint probability distribution over the variables for each timeframe. The filter is named after Rudolf E. Kálmán, one of the primary developers of its theory.
Basic concept
Kalman process chart
Variable $ Functions
- Original error estimate
- Error in estimate
- Calculate Kalman gain
- Calculate current estimate
- Previous estimate
- Error in data (measurement)
- Calculate new error in estimate
- Updates estimate
